An Improved Crow Search Algorithm to Control MPPT Under Partial Shading Conditions

Abstract

An improved crow search (ICS) nature-inspired algorithm for tracking maximum power is proposed in this paper. The objective of the proposed ICS algorithm is to mitigate the drawbacks of the conventional algorithms, such as steady-state oscillations, delayed convergence, and the inability to track maximum power peak during shading conditions. Crow search (CS) is mainly based on the intelligence factor of hidden food places. In this paper, an experience factor is introduced, which speeds up the searching process of crows and accurately detects the shade occurrence. Furthermore, this algorithm is simple and easy to implement. Matlab simulations and experimental results are performed to evaluate the performance under various shading patterns. The proposed algorithm is compared with PSO, Improved Jaya, and crow search algorithms to validate the competence. The results show that this algorithm has vast superiority in tracking global maximum power point in less convergence time.